Tracking the uptake of artificial intelligence (AI) poses a fundamental measurement challenge. Recent data, such as Eurostat’s survey, are useful but often limited in geographic and chronological coverage and can be difficult to adjust as technologies change. Given that AI usage is evolving rapidly, frequently updated data sources can help us understand the dynamics and address data gaps.

To measure AI usage in real time, the OECD.AI team has developed an approach that leverages web traffic data. Platforms such as Similarweb provide estimates of website and app visitors, offering a window into how users interact with digital services at scale.  This web traffic data enables researchers to track usage trends for AI chatbot interfaces and services.

While these data should be interpreted with appropriate methodological caveats, they offer unique insights into real-time dynamics of how the public is using AI.

A new measure of AI chatbot usage

To build a measure of monthly usage of GenAI chatbots, we focus on direct user engagement through AI web interfaces. It uses the number of unique, deduplicated visitors over a two-year period between 1 February 2024 and 30 March 2026 to the three main AI chatbot websites, ChatGPT, Claude and Gemini. According to the data, these three sites account for essentially all web traffic to chatbot interfaces in GPAI countries, as users of other sites will almost always also use one of the three leading sites and would therefore be removed during the deduplication process anyway.  

With the number of unique visitors across the three chatbots, divided by population from IMF projections, the new measure of GenAI chatbot usage is computed.

There are a few methodological limitations to this approach as a proxy for broader AI adoption. This metric focuses on consumers, capturing only AI chatbot usage, and therefore excludes embedded or API-based uses of AI, which are increasingly central to enterprise applications and productivity-enhancing workflows. Additionally, web traffic data may be difficult to estimate for smaller jurisdictions and may introduce some bias into the country-level data. The estimates rely entirely on SimilarWeb data collection, which is subject to its own panel and estimation biases.

GenAI chatbot usage has grown rapidly

Across GPAI countries, this new measure of GenAI chatbot usage has surged over the past year, from roughly 18% of the population, on average, in January 2025 to 28% in January 2026. The countries seeing the highest growth in this measure in 2025 were Japan and Türkiye, where usage rates more than doubled. Singapore is consistently the top user per capita, and also experienced the largest increase in absolute terms, rising from 36% to 63% of the population using AI chatbots in 2025. You can explore the data for yourself in (Figure 1).

Figure 1. GenAI chatbot usage rates between February 2024 and March 2026

https://chart.oecd.ai/29736

Notes: Costa Rica, Estonia, Iceland, Latvia, Lithuania, Luxembourg, Malta, Senegal, Slovenia missing due to insufficient data. GenAI usage rates use deduplicated unique visitors from ChatGPT, Claude, and Gemini. Sources: OECD.AI calculations using data from SimilarWeb and the IMF.

The intensity of use among GenAI chatbot users has also grown. Figure 2 shows that in mid-2024, the average visit length to AI chatbots across GPAI countries was about 4.5 minutes. This grew to over 5.5 minutes in early 2026, signalling more intensive consumer use.

Figure 2. Average visit duration to AI chatbots in seconds

GenAI chatbot use demographics

Younger audiences use AI chatbots the most. A deeper look at the data provides a better understanding of the demographics of AI users. First, GenAI chatbot usage remains much higher among younger consumers. According to the data, over half of people aged 25-34 in February 2026 used GenAI chatbots across GPAI countries, while only about 8% of people aged 65 and over were chatbot users (Figure 3).

Figure 3. GenAI adoption rate by age group

Usage among older consumers accounts for much of the recent growth. While individuals under 35 still account for more than half of all GenAI chatbot users in 2026, their share has gradually declined. Between February 2024 and February 2026, the share of visitors aged 35 and over increased from 38% to 48%. This suggests that much of the recent growth in AI chatbot adoption has been driven by older age groups, indicating that usage is becoming more mainstream and widespread across the broader population (Figure 4).

Figure 4. Age distribution of AI users

Recreational AI use and younger populations. Looking beyond the three most popular AI chatbots, the data show that usage varies widely across age groups. Figure 5 shows that people under the age of 25 are much more likely to use Character.AI (a free chatbot that lets you create digital characters and interact with them via text, voice messages and calls) than any other chatbot. This indicates more recreational use among that age group. Older age groups, by contrast, are much more likely to use Microsoft Copilot, perhaps due to built-in referrals and employer-funded subscriptions.

Figure 5. Share of AI Chatbot usage by age groups

Different chatbots, different jobs

Browsing patterns also indicate differences in how AI chatbots are used. Users of ChatGPT and Gemini are more likely to also visit recreational-oriented platforms such as YouTube, Instagram, and Facebook. In contrast, users of Claude and Microsoft Copilot more frequently visit sites such as LinkedIn, cloud platforms, Notion, and GitHub. This suggests that Claude and Copilot users are concentrated in professional settings, particularly for workplace productivity and coding-related tasks (Table 1). The use of Copilot, in particular, seems to be largely driven by Microsoft’s own ecosystem.

Table 1. Most relevant domains for users of top chatbots for 2025

Relevance score ranks	chatgpt.com	gemini.google.com	copilot.microsoft.com	claude.ai
1	google.com	reddit.com	bing.com	github.com
2	youtube.com	instagram.com	login.live.com	stackoverflow.com
3	instagram.com	youtube.com	msn.com	notion.so
4	reddit.com	github.com	m365.cloud.microsoft	aistudio.google.com
5	facebook.com	facebook.com	github.com	linkedin.com

Men are more likely to use GenAI chatbots, but numbers differ by country. For the most part, men use GenAI chatbots at a higher rate than women across GPAI countries, although the gap is relatively small (Figure 6). Over time, the gap has fluctuated: decreasing in 2025 before expanding again in 2026. These variations are largely due to country-specific changes: throughout 2024, there was a rapid increase in female adoption in Finland, the Czech Republic, New Zealand, Denmark and Hungary, while in 2025, there was a relatively faster increase in male adoption in Korea, the United States, Norway, Germany and Türkiye.

Figure 6. GenAI usage rate by gender

However, some countries display more pronounced differences: Korea shows the largest gender gap, with male usage rates nearly double those of women. Germany and Italy also show significantly higher usage among men. By contrast, Singapore, the GPAI country with the highest overall usage rate, and Colombia, the Czech Republic, Finland, Hungary, Ireland, Mexico, New Zealand, and Saudi Arabia are the countries where female users adopt AI chatbots at a higher rate than male users (Figure 7).

Figure 7. Male and female AI usage rates by country

How trustworthy are these estimates?

GenAI usage rates by country in this analysis are broadly in line with other measures of GenAI tool usage, such as the Eurostat survey and the Microsoft AI Economy Institute (Figure 8). In 2025, Eurostat estimated that just under 33% of people aged 16-74 in the EU had used generative AI tools in the past 3 months. Microsoft’s AI Economy Institute found an average usage rate of 30% over GPAI countries for which data are available. In our estimates, we find that 27% of the total population has used AI on average over the last quarter of 2025. Overall, our measure is strongly aligned with these benchmarks, with a statistically significant correlation of 0.63 with Eurostat estimates and an even stronger correlation of 0.78 with Microsoft’s estimates.

Figure 8. SimilarWeb GenAI usage rates correlate strongly with other estimates and offer broad coverage

As AI use becomes mainstream, usage data across countries and demographic groups provide broad insights

Taken together, these results give us a clearer view of GenAI usage and how the picture is evolving. Usage is no longer niche. In just a year, GenAI chatbot use jumped from under a fifth to almost a third of the population across GPAI countries, with countries like Singapore and the Netherlands already seeing usage rates close to or above 50%. At the same time, differences in aggregate use between countries remain significant and warrant closer attention.

AI is also moving beyond its early adopters. While younger users still lead, much of the recent growth is coming from people over 35, signalling that chatbots are becoming mainstream. At the same time, usage is not uniform: some users engage with AI for recreational purposes, while others integrate it into productivity, coding, and workplace tasks. Gender gaps exist as well, but vary widely across countries, highlighting the importance of local context.

Traditional surveys remain essential but provide snapshots and are difficult to adjust to the pace of AI change. Web traffic data helps fill that gap by showing real-time GenAI usage data, how it shifts over time, and how patterns differ across countries and groups. Combined with other indicators on OECD.AI, these data provide a more complete and timely picture of AI diffusion and uptake.

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Methodological considerations

SimilarWeb’s data comes from several sources:

• An anonymised panel of millions of Internet users with URL tracking enabled and some demographic information
• Partnerships with websites and data aggregators to directly share data on website analytics
• Online resources and public information

Our analysis relies largely on SimilarWeb’s “unique visitors” metric, defined as the total number of distinct users visiting a domain in each month. This approach involves a deduplication process that accounts for the shared visitors from each of the three most popular chatbots. ChatGPT unique visitors were spliced to account for the domain transition from openai.com. The estimates cover the vast majority of users, as users of other chatbots typically also visit one of the three captured here, and would therefore almost always be removed during the deduplication process.

These data cover the period from February 2024 to March 2026, a timeframe that captures significant developments in the AI landscape. The analysis is also further scoped down to the 37 Global Partnership on AI (GPAI) countries available on the SimilarWeb platform (see https://oecd.ai/en/about/about-gpai for more information on GPAI). As of this analysis, these countries are Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, Colombia, Czechia, Denmark, Finland, France, Germany, Greece, Hungary, India, Ireland, Israel, Italy, Japan, Korea, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Saudi Arabia, Serbia, Singapore, Slovak Republic, Spain, Sweden, Switzerland, Türkiye, United Kingdom, and the United States.

The lower adoption rates in the SimilarWeb estimates when compared to other estimates mainly reflect differences in how each source defines an AI user. First, in the Eurostat data, the distinction is not made on how the user is accessing AI and is therefore considered a broader indicator than our SimilarWeb approach, which focuses solely on AI chatbots accessed via the website (i.e. not including App users). For example, respondents to the Eurostat survey may consider the AI summaries given after Google queries to be a use of generative AI, but is not captured in our measure. Secondly, someone counts as an AI user if they used AI at least once in the past three months in the Eurostat survey, while Microsoft uses a six-month window. Our measure takes the monthly average, which means that a person who visits a chatbot only once during the three-month period (to align with Eurostat) contributes less than someone who uses AI regularly.

Ultimately, each source comes with its own strengths and limitations:

• Survey-based measures such as Eurostat have good statistical adjustments to account for demographics but can be affected by response biases and sampling uncertainty.
• Microsoft has access to real-time, proprietary AI use data, but their estimates rely on assumptions about market share and internet penetration to scale their data.
• SimilarWeb has broad geographical coverage and is based on real click-based data, but it also captures only chatbot usage and relies partly on panel-based web traffic data and its own modelling assumptions, which may be biased.

Taken together, the differences highlight why no single measure currently captures AI adoption on its own.  Our new GenAI usage estimates provide a valuable perspective: a timely, internationally comparable indicator that helps track users, their demographics, and how regularly they engage with AI chatbots in practice.

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Artificial intelligence (AI) is both a technology story and a policy challenge. Governments across sectors and regions are grappling with the same question: how to effectively support the safe, trustworthy development and use of AI in ways that align with their countries’ needs?

Whether setting a national AI strategy or designing concrete initiatives to implement it, governments need guidance that meets them where they are. From experience, I can attest that the hardest part is rarely agreeing on principles; it is finding concrete, comparable examples of how others made them work. That is the gap the OECD AI Policy Toolkit closes.

Released yesterday by the OECD under the Global Partnership on Artificial Intelligence (GPAI), the AI Policy Toolkit is the first version of a practical, non-prescriptive guide for policymakers to translate the OECD AI Principles into action—a deliberate shift from defining what good AI policy requires to showing how to build it.

What the Toolkit does

The Toolkit is an interactive, evolving platform to support policymakers throughout the AI policy cycle. It complements OECD.AI’s broader ecosystem of tools for data, analysis and AI governance.

The Toolkit helps governments target and prioritise where to act. Through AI-powered semantic search, it surfaces relevant policy examples and guidance drawn from real-world practice, turning the OECD’s accumulated evidence into options a policymaker can use the same day—rather than a library to be read.

Built with policy-makers, not just for them

A year ago, the 2025 OECD Ministerial Council Meeting set this work in motion. What followed was less a drafting exercise than a year of listening—and the Toolkit released today reflects what countries told us they needed.

Far from being a top-down exercise, the OECD Secretariat developed the Toolkit with end-users through co-creation across regions. Targeted interviews and four co-creation workshops across Southeast Asia, Latin America and Africa—one of which Costa Rica was proud to host—brought policymakers, industry and experts together to shape its design around how governments actually work and make decisions.

Not only did these co-creation workshops highlight both shared challenges and region-specific priorities. They grounded the Toolkit in fundamental policy questions:

• How to navigate trade-offs between local and global AI models, or between innovation and regulation?
• How to address infrastructure gaps, such as AI compute capacity?
• How to scale AI in agriculture, education or healthcare?

Two lessons that shaped the Toolkit

Moreover, the collaborative approach to developing the Toolkit has yielded important collective lessons.

• First, context is decisive: AI policy must reflect national needs and preferences, institutional capacity and levels of digital maturity.
• Second, addressing shared global challenges such as managing risks posed by advanced AI systems or ensuring diverse linguistic and cultural representation in AI models requires international cooperation as well as tailored policy responses.

Our sincere thanks go to the governments and organisations that, alongside Costa Rica, made this possible—notably Italy, France, Korea, Japan, the United Kingdom, the European Union, the French Development Agency and the Inter-American Development Bank—and to the policymakers and experts who contributed their time and insight. I also commend the OECD Secretariat for its sustained work.

What comes next

The OECD Ministerial Council Meeting (MCM) marks the Toolkit’s first release, which is an important milestone, but it is far from the finish line.

As AI technologies and related policy issues develop, the OECD remains dedicated to ensuring the Toolkit stays relevant through regular updates by:

• Refining and improving the Toolkit through ongoing feedback and iteration
• Incorporating more policy examples and use cases to strengthen its practical relevance via the OECD.AI Policy Navigator
• Expanding its coverage of emerging policy issues, including sector-specific guidance, infrastructure and regulatory approaches

From shared principles to shared practice

The OECD AI Policy Toolkit results from a collaborative effort to transform AI principles into implementation. By integrating OECD standards with regional insights, it guides policymakers in leveraging AI’s opportunities while responsibly and effectively managing its challenges.

The Toolkit’s success will be measured not by its launch but by the policies it helps shape. Its impact depends on sustained collaboration and support. A year from now, I expect us to point to concrete cases where this tool moved a country from principle to practice—better AI policies for better lives.

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Across major economies, trustworthy artificial intelligence is rapidly moving from high-level policy to deployment in core industries such as health, manufacturing and mobility. The European Union is positioning itself for this shift by focusing not only on innovation capacity but also on trustworthy and coordinated implementation across its Member States. Gaining a deeper understanding of where AI is already being applied and gathering evidence on determinants of adoption are essential to assess Europe’s competitiveness and policy readiness.

The European Union is pursuing its ambition to become a global leader in trustworthy AI, moving from high-level policy to on-the-ground implementation. The OECD worked closely with the European AI Office to monitor efforts to develop trustworthy AI and promote its development across the European economy, with a two-volume publication series analysing how this transition is taking place in practice. The first volume focuses primarily on national strategies, initiatives and governance mechanisms for AI in EU Member States. The second, Progress in Implementing the European Union Coordinated Plan on Artificial Intelligence (Volume 2), shifts the lens to sector-specific impact.

The report supports efforts by the European Commission and EU Member States to promote the development, deployment, and use of AI technologies across priority sectors. It draws on extensive multi-stakeholder engagement, including semi-structured interviews with industry experts and insights from dedicated stakeholder workshops. It focuses on concrete use cases that address specific needs in agriculture, healthcare, manufacturing and mobility, selected high-impact sectors where AI can contribute to digitalisation, sustainability and economic resilience. These sectors are most prominently featured across national AI strategies (Figure 1) as priority sectors for AI applications.

Agriculture: from precision to sustainability

Globally, agricultural producers are increasingly turning to AI-enabled precision tools to address labour shortages, environmental pressures and resource constraints. Within Europe, similar dynamics are shaping experimentation with AI-supported farming systems aligned with environmental targets under the European Green Deal.

As AI-driven solutions help optimise resources, reduce chemical inputs and maintain yields, the EU’s agricultural sector is exploring AI deployment to address structural workforce shortages and sustainability requirements. AI-powered agricultural robots and crop and soil monitoring systems are playing a growing role in improving resource efficiency.

Robs4Crops, for instance, illustrates how computer vision and sensor-based systems can enable autonomous mechanical weeding and spraying in vineyards, crop fields and apple orchards. AI4SoilHealth, in turn, is developing an open-access, AI-driven digital infrastructure to help assess and monitor soil health metrics across Europe.

At the same time, many initiatives remain at pilot or experimental stages. Limited digital infrastructure in rural areas, fragmented and inaccessible datasets (due to the resources required to collect high-quality, diverse data across crops, soil, and livestock, and to limited interoperability of existing public datasets), financial barriers, and uncertainty over return on investment continue to constrain large-scale adoption.

Healthcare: enhancing diagnostics and operations

Health systems worldwide are using AI to improve diagnostic accuracy and manage increasing service demand. In Europe, demographic ageing and workforce shortages are strengthening the case for deploying AI across both clinical and operational settings.

AI can help address rising costs and workforce shortages in healthcare while improving patient outcomes through faster and more accurate diagnostics.

One of the most impactful use cases is AI-enhanced medical imaging for the early detection of conditions such as cancer, supported by initiatives including the European Cancer Imaging Initiative. Similar approaches are already being deployed in the United States and Japan, where AI-assisted radiology is helping reduce diagnostic backlogs, highlighting the strategic importance of scaling comparable capabilities across Europe.

Beyond clinical care, the report explores how AI is improving hospital operations. Predictive and optimisation AI systems can help forecast patient inflows and manage bed occupancy, helping healthcare providers reduce staff pressure and waiting times. Perplex, an EU-funded initiative, illustrates how AI can help automate and optimise scheduling and resource management in the outpatient department of a hospital in Madrid.

Despite this potential, barriers such as fragmented health data environments and trust challenges remain significant constraints.

Manufacturing: the rise of industrial intelligence

Competitiveness in the manufacturing sector increasingly depends on integrating AI into production systems, supply chains, and quality control processes. While other major economies are accelerating investment in smart factories, adoption across Europe remains uneven.

AI adoption in EU manufacturing remains modest and highly fragmented, with pharmaceuticals and electronics leading the way, while traditional industries such as textiles and food processing progress more slowly.

Despite these differences, there are areas where AI could have a substantial impact. The report highlights three priority use cases: predictive maintenance, quality assurance and control, and supply chain optimisation.

Predictive maintenance systems, such as those developed through the Made in Europe Partnership, analyse sensor data to forecast equipment failures and reduce costly downtime. In quality control, AI-powered inspection improves efficiency by identifying defects in real time. Comparable smart-manufacturing deployments in East Asia and the United States demonstrate how scaling such applications can strengthen productivity growth and industrial resilience.

Mobility: navigating toward a connected future

Transport systems are becoming increasingly data-driven as cities and logistics operators deploy AI to improve safety, efficiency and sustainability. Across Europe, mobility-sector deployment is closely linked to broader digital and climate transition strategies.

AI can help transport and mobility systems become safer, more efficient and more sustainable. AI-enabled traffic management systems, such as those explored in the AI4Cities project, can reduce congestion by dynamically adjusting traffic-light patterns. Automated driving technologies and intelligent freight logistics systems can further optimise routes and scheduling efficiency. Here, the EU Connected, Cooperative and Automated Mobility (CCAM) Partnership aims to accelerate the transition from research prototypes to real-world applications.

These developments are intended to align with the Sustainable and Smart Mobility Strategy, although gaps in infrastructure readiness and investment capacity remain important constraints for many operators.

Overcoming barriers to scale

Progress in Implementing the European Union Coordinated Plan on Artificial Intelligence (Volume 2) demonstrates significant sectoral potential for AI deployment, while identifying persistent bottlenecks that continue to slow implementation. Addressing these constraints will be critical to moving from experimentation with pilots to widespread deployment that fundamentally transforms the European economy for the better. To do so, the report puts forward a number of key recommendations, including the following:

• Focus on concrete sector-specific AI use cases

Targeted policies, investment, and collaboration will be essential to unlock AI’s full potential in key sectors of the EU’s economy. Public-private-academic partnerships, open innovation platforms, and cross-border collaborations can accelerate AI development and adoption, particularly when grounded in sector-specific needs. Focusing on concrete AI use cases, building ownership and trust through transparency and co-design with end-users, and demonstrating tangible benefits will be key to ensuring that AI strengthens Europe’s economic competitiveness, sustainability, and societal well-being.

• Strengthen data foundations

Investing in high-quality datasets, common standards and shared governance frameworks can enable secure, privacy-preserving data sharing across borders and sectors. Improving data representativeness and reducing fragmentation will lower entry barriers and support downstream AI adoption.

• Expand infrastructure and compute capacity
  Investments in broadband connectivity, cloud and edge computing, 5G networks and AI compute environments—including AI factories and high-performance computing centres—are essential to bridging regional gaps. Initiatives such as EuroHPC are helping ensure that economic actors, including SMEs, can access the computational resources required to train and deploy advanced AI models.

• Close the skills and talent gap
  Unlike their larger counterparts, who tend to have more resources at their disposal, smaller firms and public organisations require access to technical expertise and sector-specific training before large-scale deployment of AI becomes feasible. European Digital Innovation Hubs (EDIHs) are supporting this process through a “test before invest” approach that lowers adoption risks.

• Enhance trust and regulatory coordination
  Regulatory sandboxes allow firms to test innovative AI applications under supervisory conditions, enabling regulatory learning and improving compliance readiness before market entry. Providing clearer guidance and harmonising regulatory interpretation across Member States will remain particularly important for start-ups and SMEs operating under the EU AI Act, alongside relevant existing rules such as the GDPR.

Many of the report’s findings align with the European Commission’s Apply AI Strategy, which focuses strongly on accelerating adoption and the active deployment of AI across the economy. The OECD and the European Commission will continue working together to support implementation of the Strategy and to ensure that lessons from European AI deployment experiences contribute to the broader global AI policy community.

The authors would like to thank John Leo Tarver for his contributions to this report series and blog posts.

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AI agents and agentic AI based on large language models are becoming more autonomous and capable of interacting with both physical and virtual environments. As the capabilities of these AI systems grow, they are gaining visibility, and with reason. It is reaching a point where they could become the driving force behind innovation, investment and improved productivity across sectors by streamlining processes and enabling more efficient operations.

While ideas related to agency have long been explored in academic research in fields such as philosophy, economics and computer science, recent advances in AI are stretching conceptual boundaries. As AI’s capabilities evolve, so do our shared understanding of what qualifies as AI agent and agentic AI.

The OECD report, The agentic AI landscape and its conceptual foundations, developed by the OECD.AI Expert Group on Agentic AI, helps clarify what AI agents and agentic AI are and how they differ. Grounded in the OECD AI system definition, the analysis examines how these terms are defined and used across the literature. By analysing key features, overlaps and distinctions and mapping them to the core elements of the OECD definition of an AI system, the report helps to establish more precise and consistent terminology. And in a rapidly evolving field, conceptual precision is essential for effective, well-informed governance.

Three key messages stand out in the report:

• AI agents and agentic AI are closely related, but not interchangeable.
• Agentic AI ought to be seen as a socio-technical paradigm.
• Despite technological gaps and varying levels of maturity in areas such as digital security and privacy, uptake is growing.

The common foundations and meaningful distinctions of AI agents and agentic AI

Our analysis shows that AI agents and agentic AI share foundational characteristics. Both involve systems with a degree of autonomy that pursue goals and can perceive and act within physical and virtual environments.

However, there are differences that mean these terms are not interchangeable.

1. AI agents can be understood as systems that perceive and act on their environment with a degree of autonomy, using tools as needed to achieve specific goals and adapt to changing inputs and contexts.
2. By contrast, agentic AI generally refers to systems composed of multiple co-ordinated AI agents that can break down tasks, collaborate and pursue complex objectives autonomously over extended periods. Agentic AI systems are designed to operate in more open-ended, less predictable physical and virtual environments, and to function with minimal human supervision.

In short, agentic AI is more complex, as it can co-ordinate multiple agents, perform task decomposition and delegation, and sustain operations over longer periods. It can also operate in more complex, less predictable environments with limited human oversight.

Agentic AI as a socio-technical paradigm

Agentic AI systems are not isolated technical artefacts. They are frequently embedded in social contexts and interactions and operate within a socio-technical paradigm.

Their value lies not only in autonomous action, but in interaction with other AI agents, humans and institutional processes. Co-ordination and negotiation across these actors require advanced reasoning capabilities, robust infrastructure and reliable communication protocols.

This relational perspective is an essential part of what agentic AI is. This means that understanding how they interact within broader ecosystems is essential to designing agentic AI systems that function responsibly and effectively, particularly in open or high-stakes environments.

Uptake is accelerating, but maturity is uneven

The report also presents descriptive evidence on trends in AI agent adoption. Many developers have already integrated them into their toolkits, and survey data indicate that nearly half of respondents on Stack Overflow use them or plan to do so.

To be clear, adoption should not be confused with maturity. Developers highlight opportunities to further strengthen the security, privacy and accuracy of AI agents. These concerns underscore an important point: as the capabilities of agentic AI advance rapidly, progress in robust, trustworthy AI systems must keep pace.

A foundation for further analysis

Overall, the report provides a descriptive overview of the agentic AI landscape, clarifying key concepts and characteristics and establishing a shared analytical foundation. By anchoring the discussion in the OECD AI system definition, it aims to promote coherence across technical and policy communities.

Looking ahead, an improved understanding of real-world use will be essential to identify where safeguards, standards, and governance mechanisms will be most effective. Policy-relevant typologies that build upon this work could help guide governance efforts to distinguish systems by level of autonomy, degree of adaptiveness, domain of operation and scale of impact. Evidence-based policymaking will require more empirical data on how AI agents and agentic AI are being adopted and used across sectors, as well as clearer evidence of their broader implications and impacts.

This report contributes to a clearer, shared understanding of agentic AI and provides a basis for thoughtful, forward-looking policy grounded in conceptual clarity. As agentic AI systems become more capable of coordinating multiple AI agents, taking action and operating over longer periods, governance conversations have to keep pace.

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Companies hoping to take advantage of AI’s opportunities need to be trustworthy. Whether investing in, developing, or using AI, the OECD’s new Due Diligence Guidance for Responsible AI provides businesses with an internationally agreed, government-backed tool to demonstrate that markets and societies can trust their AI systems.   

Recent international reporting underscores a growing consensus: AI is not just a technological shift. It is a major geopolitical, economic, and societal phenomenon that demands coordinated action amongst all actors, including companies. 

AI has the potential to transform society through productivity, economic value and solutions to complex challenges, but for these benefits to materialise, AI needs trust.  So far, the technology seems to advance faster than its guardrails. The gap between AI systems and appropriate safeguards is now one of the defining challenges for policymakers and global businesses alike. Both are under pressure to balance AI innovation and diffusion with safety and risk management. Success depends on getting the balance right.

Risks throughout the AI value chain are continually evolving

Risks to people and the environment can manifest at any point along the AI value chain. The OECD actively tracks and categorises risks through its AI Incidents and Hazards Monitor.

Here are a few examples. At one end of the AI value chain, there are the people who label, clean, and moderate the vast datasets required to train AI models. They can face low wages, long hours, and suffer psychological distress from exposure to harmful content. Companies need to ensure decent work for data enrichment workers.

The environmental costs of running AI systems can also be significant, particularly for energy and water consumption by data centres that power AI development and deployment, which may lead to higher energy prices.

Data privacy is another critical concern. AI models are trained on massive datasets that may include personal or sensitive information. If these datasets are not properly anonymised and secured, it can lead to data breaches. If AI models “memorise” and reproduce sensitive data in their outputs, they can expose confidential details, creating legal and ethical dilemmas.

At the other end of the AI value chain, the potential for AI misuse poses risks such as reputational harm and the spread of misinformation. AI-generated deepfakes, for instance, can be used to create realistic but fabricated content, damaging reputations or manipulating public opinion. Similarly, AI can be used to generate and disseminate mis and dis-information at speed and scale, eroding trust in institutions and potentially influencing events.

Worldwide, governments, consumers, and markets are calling for responsible and trustworthy AI. This is one of the reasons for the surge in mandatory and voluntary AI risk management frameworks, responsible AI initiatives, global agreements, academic research and statements from industry leaders and investors. However, this surge in frameworks is also increasing complexity for companies, as risk management is defined differently across jurisdictions and understanding of AI-related risks is evolving.

OECD Due Diligence Guidance for Responsible AI: A flexible, whole-of-value-chain approach to support businesses in navigating evolving risks and rules

This is why the OECD has now developed the first internationally agreed, government-backed Due Diligence Guidance for Responsible AI. Backed by all the OECD’s member countries, plus 17 partner governments and the EU, this Guidance helps enterprises navigate the complex terrain of AI risk management. It is designed to help businesses ensure that the AI systems they develop are trustworthy, used and developed safely and responsibly, and aligned with broad societal values.

Concretely, this Guidance offers:

• A step-by-step framework for enterprises to set up internal management systems capable of proactively identifying and responding to risks related to human rights, labour standards, and environmental impacts.
• Comprehensive coverage of all risk areas from the leading international standards that it is built on and reflects, notably, the OECD Guidelines for Multinational Enterprises on Responsible Business Conduct (MNE Guidelines) and the OECD Recommendation on Artificial Intelligence (AI Principles);
• Recommendations and implementation examples for everyone in the AI value chain, from data suppliers and infrastructure providers to financiers and end-users – including enterprises. The guidance emphasises a “whole-of-value-chain” approach to support secure and resilient AI value chains more resistant to supply chain shocks and interference.
• A roadmap of related provisions in existing frameworks, indicating how each step complements and relates to relevant provisions from AI risk management frameworks. This feature helps enterprises understand how implementing this guidance can help them meet expectations from multiple sources and navigate the current landscape of AI risk management frameworks.

Responsibility and trust can give a competitive edge

Responsibility and innovation not only coexist but also reinforce each other. Companies that show a commitment to responsible AI and actively address potential risks can gain trust from investors, customers, regulators, and policymakers. This trust leads to a competitive edge. Instead of hindering innovation, responsible AI practices can speed up growth by reducing obstacles and preventing costly damage to reputation, legal issues, and society.

Responsible and trustworthy AI is becoming increasingly crucial for accessing global markets as international regulatory and voluntary risk management frameworks evolve. Companies in the AI value chain that meaningfully implement the Guidance’s recommendations can position themselves advantageously for cross-border expansion, potentially avoiding the substantial costs of retrofitting systems to meet various regional requirements.

As AI continues to develop rapidly, frameworks and best practices for responsible AI are likely to evolve as well. To help stakeholders keep pace, the OECD will launch an online navigation tool later this year with updates on new frameworks and use cases.

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